Portrait of Prof. Andreas Müller  

Andreas C. Müller


410 S.W. Mudd

Tel(212) 939-7045

Research Interests

Machine learning software, explainable machine learning algorithms, automatic machine learning.

Müller works on providing easy-to-use tools for scientists to apply machine learning methods, and provide educational material around machine learning, with the goal of lowering the barrier of entry to data driven research.

Müller received a Diploma in Mathematics in 2009 and his PhD in 2014 in Computer science, both from the University of Bonn in Germany.


  • Assistant research scientist at NYU Center for Data Science, 2014-2016


  • Lecturer in data science, Columbia University, 2016-
  • Machine learning scientist, Amazon Development Center, Germany, 2013-2014


  • Müller, A and Guido, S. (2016) Introduction to Machine Learning with Python, O’Reilly.
  • Varoquaux, G., L. Buitinck, G. Louppe, O. Grisel, F. Pedregosa, and A. Müller (2015). Scikit-learn: Machine Learning Without Learning the Machinery. GetMobile: Mobile Computing and Communications
  • Abraham, A., F. Pedregosa, M. Eickenberg, P. Gervais, A. Müller, J. Kossaifi, A. Gramfort, B. Thirion, and G. Varoquaux (2014). Machine learning for neuroimaging with scikit-learn. Frontiers in Neuroinformatics.
  • Müller, A. and S. Behnke (2014). PyStruct: Structured Prediction in Python, Journal of Machine Learning Research.
  • Müller, A. and S. Behnke (2014). Learning Depth-Sensitive Conditional Random Fields for Semantic Segmentation of RGB-D Images. In: Proceedings of the International Conference of Robotics and Automation (ICRA).
  • Müller, A., S. Nowozin, and C. Lampert (2012). Information Theoretic Clustering Using Minimum Spanning Trees. In: Proceedings of DAGM / OAGM, pp.205–215.
  • Scherer, D., A. Müller, and S. Behnke (2010). Evaluation of pooling operations in convolutional architectures for object recognition. In: Proceedings of the Interntional Conference on Artificial Neural Networks (ICANN). Springer, pp.92–101.